Development of Distress Guidelines and Condition Rating to Improve Network Management in Ontario, Canada
Bibliographic record
Abstract
In 2006, the Ministry of Transportation of Ontario, Canada (MTO), completed a study with the Centre for Pavement and Transportation Technology at the University of Waterloo to evaluate the performance of automated and semiautomated technologies that collect pavement distress data. From that study it was recommended that MTO define concise guidelines for surveying pavement distresses at the network level by using automated collection technologies and semiautomated distress analysis and for the guidelines to give special attention to quality assurance. In light of that recommendation, the study detailed in this paper presents the development of pavement distress guidelines and a distress manifestation index (DMI) for network-level (DMI NL ) evaluations by using automated collection technologies and semiautomated distress analysis. To define and validate DMI NL , sections evaluated in the previous study were considered. The relative effect of each distress was obtained by linear regression and statistical analysis. The principle used to define the weighting factors was that the distresses considered by the new guidelines should quantify with a minimum error the DMI estimated by the MTO traditional method.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.006 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".